The paper deals with estimating transfer functions of stable linear time-invariant systems under stochastic assumptions. We adopt a nonparametric minimax approach for measuring estimation accuracy. The quality of an estimator is measured by its worst case error over a family of transfer functions. The families with polynomially and exponentially decaying impulse response sequences are considere...

The paper explores the problem of turbo equalisation and channel estimation under class-A impulsive noise. The channel is a frequency selective fading channel in which its time varying coefficients are expanded into a finite number of basis sequences and time invariant (TI) expansion parameters. Instead of the application of a maximum likelihood (ML) approach in its standard form, the proposed ...

In this paper, we show a simple way to derive the p-shift finite impulse response (FIR) unbiased estimator (UE) recently proposed by Shmaliy for time-invariant discrete-time state-space models. We also examine its iterative Kalman-like form. We conclude that the Kalman-like algorithm can serve efficiently as an optimal estimator with large averaging horizons. It has better engineering features ...

In the context of radar detection, the clutter covariance matrix estimation is an important point to design optimal detectors. While the Gaussian clutter case has been extensively studied, the new advances in radar technology show that non-Gaussian clutter models have to be considered. Among these models, the spherically invariant random vector modelling is particularly interesting since it inc...

A simple and effective object recognition scheme is to represent and match images on the basis of color histograms. To obtain robustness against varying imaging circumstances (e.g. a change in illumination, object pose, and viewpoint), color histograms are constructed from color invariants. However, in general, color invariants are negatively affected by sensor noise due to the instabilities of...

In this paper, we develop in part and review various iterative unbiased finite impulse response (UFIR) algorithms (both direct and two-stage) for the filtering, smoothing, and prediction of time-varying and time-invariant discrete state-space models in white Gaussian noise environments. The distinctive property of UFIR algorithms is that noise statistics are completely ignored. Instead, an opti...

A b s t r a c t. The unbiased estimator of risk of the orthogonally invariant es-timator of the skew-symmetric normal mean matrix is obtained, and a class of minimax estimators and their order-preserving modification are proposed. The estimators have applications in paired comparisons model. A Monte Carlo study to compare the risks of the estimators is given.

The ratio-type estimators have been introduced for estimating the mean and total population, but in recent years based on the ratio methods several estimators for population variance have been proposed. In this paper two families of estimators have been suggested and their approximation mean square error (MSE) have been developed. In addition, the efficiency of these variance estimators are com...

We show that the proportional likelihood ratio model proposed recently by Luo & Tsai (2012) enjoys model-invariant properties under certain forms of nonignorable missing mechanisms and randomly double-truncated data, so that target parameters in the population can be estimated consistently from those biased samples. We also construct an alternative estimator for the target parameters by maximiz...

This paper joins polynomial chaos theory with Bayesian estimation to recursively estimate the states and unknown parameters of asymptotically stable, linear, time invariant, state-space systems. This paper studies the proposed algorithms from a pole/zero locations perspective. The estimator has fixed pole locations that are independent of the estimation algorithm (and the estimated variables). ...